PET Reconstruction Research Articles

Diffusion transformer model with compact prior for low-dose PET reconstruction

Objective.Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes.Approach.In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion model with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and alternating direction method of multipliers enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks.Main results. Experimental results validate the effectiveness of DTM in reconstructing low-dose PET image quality. DTM achieves state-of-the-art performance across various metrics, including PSNR, SSIM, NRMSE, CR, and COV, demonstrating its ability to reduce noise while preserving critical clinical details such as lesion structure and texture. Compared with baseline methods, DTM delivers best results in denoising and lesion preservation across various low-dose levels, including 10%, 25%, 50%, and even ultra-low-dose level such as 1%. DTM shows robust generalization performance on phantom and patient datasets, highlighting its adaptability to varying imaging conditions.Significance. This approach reduces radiation exposure while ensuring reliable imaging for early disease detection and clinical decision-making, offering a promising tool for both clinical and research applications.

Qualitative and quantitative analysis of reduced bed position acquisition time on FDG PET image quality.

The study aim was to evaluate whether reducing bed position acquisition time would result in significant detriment to image quality. Secondary aims were to compare effect of time of flight (TOF) and Q.Clear reconstructions and patient BMI on image quality. Fluorodeoxyglucose PET-CT performed in 30 patients on a new scanner at our institution between March and May 2024 was retrospectively evaluated. Four PET reconstructions were performed: (a) 1 min 45 s TOF, (b) 2 min TOF, (c) 1 min 45 s Q.Clear, and (d) 2 min Q.Clear. For qualitative analysis, four maximum intensity projection images were evaluated side-by-sideusing a five-point visual score (1 = non-diagnostic, 5 = excellent). For quantitative analysis, liver signal-to-noise ratio (SNR) was calculated. A statistically significant reduction in visual score occurred when reducing bed position time from 2 min to 1 min 45 s (mean TOF scores 0.24 reduction, P = 0.0002; mean Q.Clear scores 0.04 reduction, P = 0.02. There was also a statistically significant difference in liver SNR when reducing bed position time. Deterioration in image quality was minimised when bed position acquisition time was reduced if Q.Clear construction was utilized. This could facilitate increased scanning capacity without clinical detriment.

Enhancing the Diagnostic Accuracy of Amyloid PET: The Impact of MR-Guided PET Reconstruction.

18 F-Florbetaben (FBB) uptake in the supratentorial cortex is indicative of amyloid positivity. Due to PET's low spatial resolution, image noise, and spill-over of signal from adjacent white-matter into gray-matter, there are inconsistencies in ratings among trained readers. A set of 264 18 F-Florbetaben (amyloid) PET/MRI exams were reconstructed using conventional ordered subset expectation maximization (OSEM) method and MR-guided block sequential regularized expectation maximization (MRgBSREM) method. Images from 264 patients reconstructed by OSEM method and rated by 3 trained readers. Fifty-three exams were rated inconsistently and were mixed with another 53 exams which were rated consistently. These 106 subjects were then rated by our readers using the MRgBSREM PET reconstruction method. Centiloids (CL) were measured using both reconstruction methods. Signal to noise ratio (SNR) was calculated in frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions for both reconstruction methods. There is significant correlation between CL measured by OSEM and MRgBSREM methods with R2=0.99. MRgBSREM enhanced the SNR in all regions by average of 21%. The number of inconsistent exams dropped by 64% using MRgBSREM method as compared with OSEM method. Using Fleiss-Kappa statistical test, the agreement between readers was raised from "Fair" to "Significant" in the 106-subjects subset. PET reconstruction with MR priors can significantly improve the consistency of ratings among trained readers. Given the prevalence of inconsistent ratings in amyloid PET, methods that enhance the ability to distinguish intermediate amyloid levels could be valuable for the widespread adoption of this modality.

Multi-angle acquisition and 3D composite reconstruction for organ-targeted PET using planar detectors.

This study investigates a multi-angle acquisition method aimed at improving image quality in organ-targeted PET detectors with planar detector heads. Organ-targeted PET technologies have emerged to address limitations of conventional whole-body PET/CT systems, such as restricted axial field-of-view (AFOV), limited spatial resolution, and high radiation exposure associated with PET procedures. The AFOV in organ-targeted PET can be adjusted to the organ of interest, minimizing unwanted signals from other parts of the body, thus improving signal collection efficiency and reducing the dose of administered radiotracer. However, while planar detector PET technology allows for quasi-3D image reconstruction due to the separation between detector heads, it suffers from degraded axial spatial resolution and, consequently, reduced recovery coefficients (RCs) along the axial direction perpendicular to the detectors. The purpose of this study was to evaluate the concept of multi-angle image acquisition with two planar PET detectors and composite full 3D image reconstruction. This leverages data collection from multiple polar angles to improve the axial spatial resolution in the direction perpendicular to the detector heads. In such, the concept allows to overcome the intrinsic limitations of planar detectors in axial resolution. This study evaluates the improvement in the quality of images acquired with the Radialis organ-targeted PET camera through multi-angle image acquisition, in both experimental and simulated imaging scenarios. This includes the use of custom-made phantom with fillable spherical hot inserts, the NEMA NU4-2008 image quality (IQ) phantom, and simulations with a digital brain phantom. The analysis involves the comparison of line profiles drawn through the spherical hot inserts, image uniformity, RCs, and the reduction of smearing observed in the axial planes with and without the multi-angle acquisition strategy. Significant improvements were observed in reducing smearing, enhancing image uniformity, and increasing RCs using the evaluated multi-angle acquisition method. In the composite images, the hot spheres appear more symmetrical in all planes. The image uniformity, calculated from the IQ phantom, improves from 7.79% and 10.98%, as measured in the images from the individual acquisitions, to 2.72% in the composite image. There is also an overall improvement in the RCs as measured from the hot rods of the IQ phantom. Furthermore, the simulation study using the digital human brain phantom demonstrates minimal smearing in the four-angle scan, as opposed to a two-angle scan. The multi-angle acquisition method offers a promising approach to transform planar PET detector technology into a true tomographic organ-targeted PET system and to enable improvement in image quality while preserving a versatility inherent to planar detector technology. Future research will focus on optimizing the multi-angle imaging protocol, including adjustments to detector separations, number of acquisition angles, and reconstruction iterations, alongside incorporating TOF, and reconstruction with point spread function modeling to further improve image quality.

Trapezoidal back projection for positron emission tomography reconstruction.

In the back projection step of the 3D PET reconstruction, all Lines of Responses (LORs) that go through a given voxel need to be identified and included in an integral. The standard Monte Carlo solution to this task samples stochastically the surfaces of the detector crystals and the volume of the voxel to search for valid LORs. To get a low noise Monte Carlo estimate, the number of samples needs to be very high, making the computational cost of the projection significant. In this paper, a novel deterministic projection algorithm called trapezoidal back projection (TBP) is proposed that replaces the extensive Monte Carlo sampling. Its goal is to determine all LORs that contribute to a given voxel together with their exact contribution weights. This is achieved by trapezoidal rasterization and a pre-computed look-up table. The precision and speed of the proposed TBP algorithm were compared to that of the Monte Carlo back projection of 1000, 10,000 and 100,000 samples. Measurements were run on a National Electrical Manufacturers Association (NEMA) NU 4-2008 image quality phantom as well as on a mouse acquisition. Results show that the TBP algorithm achieves the same low noise level (2.5 Uniformity %STD) as the Monte Carlo method with the highest sample number, but 13 times faster-the highest-precision Monte Carlo back projection takes 31.3 s, while TBP takes only 2.3 s on the NEMA NU 4-2008 image quality phantom of voxels. The proposed deterministic TBP algorithm achieves a low noise level in a short runtime, thus it can be a promising solution for the back projection of the 3D PET reconstruction. Its performance advantage could be used to reduce either the reconstruction time, the data acquisition time, or the noise level of the image.

Validation and clinical impact of motion-free PET imaging using data-driven respiratory gating and elastic PET-CT registration.

Clinical whole-body (WB) PET images can be compensated for respiratory motion using data-driven gating (DDG). However, PET DDG images may still exhibit motion artefacts at the diaphragm if the CT is acquired in a different respiratory phase than the PET image. This study evaluates the combined use of PET DDG and a deep-learning model (AIR-PETCT) for elastic registration of CT (WarpCT) to the non attenuation- and non scatter-corrected PET image (PET NAC), enabling improved PET reconstruction. The validation cohort included 20 patients referred for clinical FDG PET/CT, undergoing two CT scans: a free respiration CTfree and an end-expiration breath-hold CTex. AIR-PETCT registered each CT to the PET NAC and PET DDG NAC images. The image quality of PET and PET DDG images reconstructed using CTs and WarpCTs was evaluated by three blinded readers. Additionally, a clinical impact cohort of 20 patients with significant "banana" artefacts from FDG, PSMA, and DOTATOC scans was assessed for image quality and tumor-to-background ratios. AIR-PETCT was robust and generated consistent WarpCTs when registering different CTs to the same PET NAC. The use of WarpCT instead of CT consistently led to equivalent or improved PET image quality. The algorithm significantly reduced "banana" artefacts and improved lesion-to-background ratios around the diaphragm. The blinded clinicians clearly preferred PET DDG images reconstructed using WarpCT. AIR-PETCT effectively reduces respiratory motion artefacts from PET images, while improving lesion contrast. The combination of PET DDG and WarpCT holds promise for clinical application, improving PET image evaluation and diagnostic confidence.

MR-Guided PET Reconstruction: A Potential Advancement for Patients With Epilepsy.

We report a case of a 33-year-old man with epilepsy and equivocal EEG, MRI signs of mesiotemporal sclerosis, and nondiagnostic standard FDG-PET imaging. The patient underwent repeat FDG-PET/MRI to clarify the sidedness of the epileptogenic focus and to confirm the suspected MTS. The standard PET reconstruction using block sequential regularized expectation maximization failed to provide evidence of a clear epileptogenic focus. However, using MR-guided PET reconstruction, circumscribed hypometabolism was observed in the right-sided entorhinal cortex, compatible with the epileptogenic focus. The MR-guided PET reconstruction provided significantly improved gray/white matter differentiation, enhancing confidence in imaging interpretation.

Head-to-head comparison of 18F-sodium fluoride coronary PET imaging between a silicon photomultiplier with digital photon counting and conventional scanners

BackgroundWe compared silicone photomultipliers with digital photon counting (SiPM) and photomultiplier tubes (PMT) PET in imaging coronary plaque activity with 18F-sodium fluoride (18F-NaF) and evaluated comprehensively SiPM PET reconstruction settings. MethodsIn 25 cardiovascular disease patients (mean age 67±12 years), we conducted 18F-NaF PET on a SiPM (Biograph Vision) and conventional PET (Discovery 710) on the same day as part of a prospective clinical trial (NCT03689946). Following administration of 250 MBq of 18F-NaF, patients underwent a contrast-enhanced CT angiography and a 30-min PET acquisition in list mode on each PET consecutively. Image noise was defined as mean standard deviation of blood pool activity within the left atria. Target-to-background ratio (TBR) and signal-to-noise ratio (SNR) were measured within the whole-vessel tubular 3-dimensional volumes of interest on the cardiac motion and attenuation corrected 18F-NaF PET images using dedicated software. ResultsThere were significant differences in image noise and background activity between the two PETs (Image noise (%), PMT: 7.6±3.7 vs. SiPM: 4.0±2.3, p<0.001; background activity, PMT: 1.4±0.4 vs. SiPM: 1.0±0.3, p<0.001). Similarly, the SNR and TBR were significantly higher in vessels scanned with the SiPM PET (SNR, PMT: 16.3±11.5 vs. SiPM: 32.7±29.8, p<0.001; TBR, PMT: 0.8±0.4 vs. SiPM: 1.1±0.6, p<0.001). SiPM PET image reconstruction with a 256 matrix, 1.4 mm pixel, and 2 mm Gaussian filter provided best tradeoff in terms of maximal SNR, TBR and clinically practical file size. ConclusionsIn 18F-NaF coronary PET imaging, the SiPM PET showed superior image contrast and less image noise compared to PMT PET.

PIPET: A Pipeline to Generate PET Phantom Datasets for Reconstruction Based on Convolutional Neural Network Training

There has been a strong interest in using neural networks to solve several tasks in PET medical imaging. One of the main problems faced when using neural networks is the quality, quantity, and availability of data to train the algorithms. In order to address this issue, we have developed a pipeline that enables the generation of voxelized synthetic PET phantoms, simulates the acquisition of a PET scan, and reconstructs the image from the simulated data. In order to achieve these results, several pieces of software are used in the different steps of the pipeline. This pipeline solves the problem of generating diverse PET datasets and images of high quality for different types of phantoms and configurations. The data obtained from this pipeline can be used to train convolutional neural networks for PET reconstruction.

Development of quantitative PET/MR imaging for measurements of hepatic portal vein input function: a phantom study

BackgroundAccurate pharmacokinetic modelling in PET necessitates measurements of an input function, which ideally is acquired non-invasively from image data. For hepatic pharmacokinetic modelling two input functions need to be considered, to account for the blood supply from the hepatic artery and portal vein. Image-derived measurements at the portal vein are challenging due to its small size and image artifacts caused by respiratory motion. In this work we seek to demonstrate, using phantom experiments, how a dedicated PET/MR protocol can tackle these challenges and potentially provide input function measurements of the portal vein in a clinical setup.MethodsA custom 3D printed PET/MR phantom was constructed to mimic the liver and portal vein. PET/MR acquisitions were made with emulated respiratory motion. The PET/MR imaging protocol consisted of high-resolution anatomical MR imaging of the portal vein, followed by a PET acquisition in parallel to a dedicated motion-tracking MR sequence. Motion tracking and deformation information were extracted from PET data and subsequently used in PET reconstruction to produce dynamic series of motion-free PET images. Anatomical MR images were used post PET reconstruction for partial volume correction of the input function measurements.ResultsReconstruction of dynamic PET data with motion-compensation provided nearly motion-free series of PET frame data, suitable for image derived input function measurements of the portal vein. After partial volume correction, the individual input function measurements were within a 16.1% error range from the true activity in the portal vein compartment at the time of PET acquisition.ConclusionThe proposed protocol demonstrates clinically feasible PET/MR imaging of the liver for pharmacokinetic studies with accurate quantification of the portal vein input function, including correction for respiratory motion and partial volume effects.

Whole-Body Multiparametric PET in Clinical Oncology: Current Status, Challenges, and Opportunities.

The interpretation of clinical oncologic PET studies has historically used static reconstructions based on SUVs. SUVs and SUV-based images have important limitations, including dependence on uptake times and reduced conspicuity of tracer-avid lesions in organs with high background uptake. The acquisition of dynamic PET images enables additional PET reconstructions via Patlak modeling, which assumes that a tracer is irreversibly trapped by tissues of interest. The resulting multiparametric PET images capture a tracer's net trapping rate and apparent volume of distribution, separating the contributions of bound and free tracer fractions to the PET signal captured in the SUV. Potential benefits of multiparametric PET include higher quantitative stability, superior lesion conspicuity, and greater accuracy for differentiating malignant and benign lesions. However, the imaging protocols necessary for multiparametric PET are inherently more complex and time intensive, despite the recent introduction of automated or semiautomated scanner-based reconstruction packages. In this Review, we examine the current state of multiparametric PET in whole-body oncologic imaging. We summarize the Patlak method and relevant tracer kinetics, discuss clinical workflows and protocol considerations, and highlight clinical challenges and opportunities. We aim to help oncologic imagers make informed decisions about whether to implement multiparametric PET in their clinical practices.

Comment on 'A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging'.

We read with great interest the paper by Limet al(2018Phys. Med. Biol.63035042) on bias reduction in Y-90 PET imaging. In particular, they proposed a new formulation of the tomographic reconstruction problem that enforces non-negativity in projection space as opposed to image space. We comment on the algorithm they derived from this formulation and bring some clarifications on the constraint that this algorithm respects.

Effects of PET image reconstruction parameters and tumor-to-background uptake ratio on quantification of PET images from PET/MRI and PET/CT systems

Introduction: PET/CT and PET/MRI are valuable multimodality imaging techniques for visualizing both functional and anatomical information. The most used PET reconstruction algorithm is Ordered Subset Expectation Maximization (OSEM). In OSEM, the image noise increases with increased number of iterations, and the reconstruction needs to be stopped before complete convergence. The Bayesian penalized likelihood (BPL) algorithm, recently introduced, uses a noise penalty factor (β) to achieve full convergence while controlling noise. This study aims to evaluate how reconstruction algorithms and lesion radioactivity levels affect PET image quality and quantitative accuracy across three different PET systems. Materials and Methods: A NEMA phantom was filled with 18F and scanned by one PET/MRI and two PET/CT systems with sphere-to-background concentration ratio (SBR) of 2:1, 4:1, or 10:1. PET images were reconstructed with OSEM or BPL with TOF. The number of iterations and β-values were varied, while the matrix size, number of subsets, and filter size remained constant. Contrast recovery (CR) and background variability (BV) were measured in images. Results: CR increased with increased sphere size and SBR. CR and BV decreased with increased β for the 10mm sphere. Increased number of iterations in OSEM showed increased BV with limited variation in CR. BPL gave higher CR and lower BV values than OSEM. The optimal reconstruction was BPL with β between 150 and 350, where BPL was available, and OSEM with two iterations and 21 subsets for the PET/CT without BPL. Conclusion: BPL outperforms OSEM, and SBR significantly influences tracer uptake quantification in small lesions. Future studies should explore the clinical implications of these findings on diagnosis, staging, prognosis, and treatment follow-up.

Optimization of reconstruction in quantitative brain PET images: Benefits from PSF modeling and correction of edge artifacts.

Modern PET reconstruction algorithms incorporate point-spread-function (PSF) correction to mitigate partial volume effect. However, PSF correction can introduce edge artifacts that lead to quantification errors. Consequently, current international guidelines advise against using PSF correction in brain PET reconstruction. We aimed to investigate PSF-induced quantification errors in recent digital PET systems and identify conditions that mitigate them. This study utilized brain PET imaging with alginate-based realistic phantoms, simulating lesion-to-background activity ratios of 10:1 and 2:1, with eleven reconstruction parameter sets. Phantoms were prepared using a commercial anthropomorphic head phantom and two homemade inserts. Each insert contained a homogeneous 18F-FDG alginate background with hot spheres of varying diameter (3, 4, 6, 8, 10, 12, and 15mm). PET imaging was conducted on a digital PET-CT system Biograph Vision 600 (Siemens), with a 10 min scan duration. Imaging was performed with and without PSF correction, with 2, 4, 6, 12, 18, or 24 iterations in reconstruction, and with or without additional Gaussian postfiltering. We assessed the recovery coefficient (RC), contrast recovery coefficient (CRC), variability, and CRC-to-variability ratios for each sphere size and reconstruction parameter set. PSF-corrected images of the 10:1 spheres exhibited a nonmonotonic CRC-to-sphere diameter relationship due to edge artifacts overshoot in the 10mm-diameter sphere. In contrast, PSF images of the 2:1 spheres showed a monotonically increasing relationship. Non-PSF images of both phantoms showed an expected monotonically increasing CRC-to-sphere diameter relationship but with lower CRC values compared to PSF images. The nonmonotonic relationship observed with 10:1 spheres was mitigated by applying a 3-mm FWHM Gaussian postfiltering. For both phantoms, reconstructions with 6 iterations, PSF correction, and additional 3-mm FWHM Gaussian postfiltering demonstrated the highest CRC-to-variability ratios. Our findings indicate that Gaussian postfiltering suppresses PSF artifacts. This parameter set corrected the nonmonotonic CRC-to-sphere diameter relationship and improved the CRC-to-variability ratio compared to non-PSF reconstructions. Therefore, to enhance lesion detectability without compromising quantification accuracy, PSF correction coupled with Gaussian postfiltering should be used in brain PET.

Exploring de-anonymization risks in PET imaging: Insights from a comprehensive analysis of 853 patient scans

Due to their high resolution, anonymized CT scans can be reidentified using face recognition tools. However, little is known regarding PET deanonymization because of its lower resolution. In this study, we analysed PET/CT scans of 853 patients from a TCIA-restricted dataset (AutoPET). First, we built denoised 2D morphological reconstructions of both PET and CT scans, and then we determined how frequently a PET reconstruction could be matched to the correct CT reconstruction with no other metadata. Using the CT morphological reconstructions as ground truth allows us to frame the problem as a face recognition problem and to quantify our performance using traditional metrics (top k accuracies) without any use of patient pictures. Using our denoised PET 2D reconstructions, we achieved 72% top 10 accuracy after the realignment of all CTs in the same reference frame, and 71% top 10 accuracy after realignment and mixing within a larger face dataset of 10, 168 pictures. This highlights the need to consider face identification issues when dealing with PET imaging data.

Comparison of Measurement and Prognostic Power of SUV Between High-Definition and Standard PET Imaging in Non-Small Cell Lung Cancer Patients.

This study aimed to evaluate the measurement and prognostic ability of the SUVmax of whole-body tumors (SUVmaxwb) in non-small cell lung cancer (NSCLC) patients, comparing high-definition (HD) PET imaging with standard-definition (SD) PET imaging. Methods: The study included 242 consecutive NSCLC patients who underwent baseline 18F-FDG PET/CT from April 2018 to January 2021. Two imaging techniques were used: HD PET (using ordered-subsets expectation maximization with point-spread function modeling and time-of-flight techniques and smaller voxels) and SD PET (with ordered-subsets expectation maximization and time-of-flight techniques). SUVmaxwb was determined by measuring all the tumor lesions in the whole body, and tumor-to-background ratio (TBR) was calculated using the background SUVmean of various body parts. Results: The patient cohort had an average age of 68.3 y, with 59.1% being female. During a median follow-up of 29.6 mo, 83 deaths occurred. SUVmaxwb was significantly higher in HD PET than SD PET, with respective medians of 17.4 and 11.8. The TBR of 1,125 tumoral lesions was also higher in HD PET. Univariate Cox regression analysis showed that SUVmaxwb from both HD and SD PET were significantly associated with overall survival. However, after adjusting for TNM (tumor, node, metastasis) stage, only SUVmaxwb from SD PET remained significantly associated with survival. Conclusion: HD PET imaging in NSCLC patients yields higher SUVmaxwb and TBR, enhancing tumor visibility. Despite this, its prognostic value is less significant than SD PET after adjusting clinical TNM stage. Thus, consideration should be given to using HD PET reconstruction to increase tumor visibility, and SD PET is recommended for NSCLC patient prognostication and therapeutic evaluation, as well as for the classification of lung nodules.

Technical note: Can photon-counting CT improve PET/CT's quantitative accuracy?

Linear attenuation coefficients (LACs) in positron emission tomography combined with computed tomography (PET/CT) are derived from CT scans that utilize energy-integrating detectors (EID-CT). These LACs are inaccurate when iodine contrast has been injected. Photon counting detector CT (PCD-CT) may be able to improve the accuracy. To investigate whether PCD-CT can improve PET/CT quantitative accuracy. Two experiments were performed: one with CT only and one that combined PET and CT. The first experiment used an electron density phantom whose inserts were imaged with EID-CT and PCD-CT. The inserts simulated normal human tissues, including bone and iodinated blood. In the case of PCD-CT, virtual-monoenergetic images at 190keV were created. LACs were derived in each case and compared against known values. For inserts with iodine, more accurate LACs were expected with PCD-CT. The second experiment involved a custom PET phantom with various materials simulating human tissues (blood, iodinated blood, and bone) and 18F radioactivity. Data were first acquired with an EID-CT-based PET/CT system and then separately in a PCD-CT system without PET. PET images were reconstructed using LAC from EID-CT and PCD-CT. PET image values were compared against known activity values using recovery coefficients (RC). In the first experiment, LAC based on EID-CT were in error by as much as 18%, whereas the corresponding PCD-CT based measurements were within 3%. In the second experiment, minimum, maximum, and mean RC were (96.1%, 115.4%, and 103.8%) for the EID-CT method, and (95.8%, 105.5%, and 101.0%) for the PCD-CT method. The consistency of PET images in body and head orientations was improved. PCD-CT can acquire the information needed for accurate LAC for PET reconstruction in a single spiral acquisition.

The effects of back-projection variants in BPF-like TOF PET reconstruction using CNN filtration - Based on simulated and clinical brain data.

The back-projection strategies such as confidence weighting (CW) and most likely annihilation position (MLAP) have been adopted into back-projection-and-filtering-like (BPF-like) deep reconstruction model and shown great potential on fast and accurate PET reconstruction. Although the two methods degenerate to an identical model at the time resolution of 0ps, they represent two distinct approaches at the realistic time resolutions of current commercial systems. There is a lack of a systematic and fair assessment on these differences. This work aims to analyze the impact of back-projection variants on CNN-based PET image reconstruction to find the most effective back-projection model, and ultimately contribute to accurate PET reconstruction. Different back-projection strategies (CW and MLAP) and different angular view processing methods (view-summed and view-grouped) were considered, leading to the comparison of four back-projection variants integrated with the same CNN filtration model. Meanwhile, we investigated two strategies of physical effect compensation, either introducing pre-corrected data as the input or adding a channel of attenuation map to the CNN model. After training models separately on Monte-Carlo-simulated BrainWeb phantoms with full dose (events=3×107), we tested them on both simulated phantoms and clinical brain scans with two dosage levels. For the performance assessment, peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) were used to evaluate the pixel-wise error, structural similarity index (SSIM) to evaluate the structural similarity, and contrast recovery coefficient (CRC) in manually selected ROI to compare the region recovery. Compared to two MLAP-based histo-image reconstruction models, two CW-based back-projected image methods produced clearer, sharper, and more detailed images, from both simulated and clinical data. For angular view processing methods, view-grouped histo-image improved image quality, while view-grouped cwbp-image showed no advantage except for contrast recovery. Quantitative analysis on simulated data demonstrated that the view-summed cwbp-image model achieved the best PSNR, RMSE, SSIM, while the 8-view cwbp-image model achieved the best CRC in lesions and the white matter. Additionally, the multi-channel input model including the back-projection image and attenuation map was proved to be the most efficient and simplest method for compensating for physical effects for brain data. Applying Gaussian blur to the histo-image yielded images with limited improvement. All above results hold for both the half-dose and the full-dose cases. For brain imaging, the evaluation based on metrics PSNR, RMSE, SSIM, and CRC indicates that the view-summed CW-based back-projection variant is the most effective input for the BPF-like reconstruction model using CNN filtration, which can involve the attenuation map through an additional channel to effectively compensate for physical effects.

Virtual cylindrical PET for efficient DOI image reconstruction with sub-millimetre resolution

Objective. Image reconstruction in high resolution, narrow bore PET scanners with depth of interaction (DOI) capability presents a substantial computational challenge due to the very high sampling in detector and image space. The aim of this study is to evaluate the use of a virtual cylinder in reducing the number of lines of response (LOR) for DOI-based reconstruction in high resolution PET systems while maintaining uniform sub-millimetre spatial resolution. Approach. Virtual geometry was investigated using the awake animal mousePET as a high resolution test case. Using GEANT4 Application for Tomographic Emission (GATE), we simulated the physical scanner and three virtual cylinder implementations with detector size 0.74 mm, 0.47 mm and 0.36 mm (vPET1, vPET2 and vPET3, respectively). The virtual cylinder condenses physical LORs stemming from various crystal pairs and DOI combinations, and which intersect a single virtual detector pair, into a single virtual LOR. Quantitative comparisons of the point spread function (PSF) at various positions within the field of view (FOV) were compared for reconstructions based on the vPET implementations and the physical scanner. We also assessed the impact of the anisotropic PSFs by reconstructing images of a micro Derenzo phantom. Main results. All virtual cylinder implementations achieved LOR data compression of at least 50% for DOI PET reconstruction. PSF anisotropy in radial and tangential profiles was chiefly influenced by DOI resolution and only marginally by virtual detector size. Spatial degradation introduced by virtual cylinders was most prominent in the axial profile. All virtual cylinders achieved sub-millimetre volumetric resolution across the FOV when 6-bin DOI reconstructions (3.3 mm DOI resolution) were performed. Using vPET2 with 6 DOI bins yielded nearly identical reconstructions to the non-virtual case in the transaxial plane, with an LOR compression factor of 86%. Resolution modelling significantly reduced the effects of the asymmetric PSF arising from the non-cylindrical geometry of mousePET. Significance. Narrow bore and high resolution PET scanners require detectors with DOI capability, leading to computationally demanding reconstructions due to the large number of LORs. In this study, we show that DOI PET reconstruction with 50%–86% LOR compression is possible using virtual cylinders while maintaining sub-millimetre spatial resolution throughout the FOV. The methodology and analysis can be extended to other scanners with DOI capability intended for high resolution PET imaging.

Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses.

Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.